First, for the sake of fairness and correctness, we would like to point out that the approach in [1]} is not “simply attaching a regressor to a pretrained object classifier”. Such a definition deliberately ignores the overall architecture of EEG feature learning.
Second, it is well-known in the machine learning community that the success of the classification depends mainly on the quality of the representation. Thus, even “very simple regressors” may work if the features are descriptive enough of the input data.
Third, [2]} includes again unverified opinions that are simply not consistent with the state of the art. For example, Li et al. [2]} ignore two recent works by Kamitani et al. [4]} (published in Nature Communication) and Cichy et al. [5]} (appeared in Nature Scientific reports) that employ linear regression models to learn cross-modal neural and visual information for classification. In particular, [4]} used linear regression from fMRI data to visualize representations learned by a pre-trained object classifier — thus contradicting [2]}'s very claim.
